Proceedings ArticleDOI
Automated modeling and robotic grasping of unknown three-dimensional objects
Gary M. Bone,A. Lambert,M. Edwards +2 more
- pp 292-298
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TLDR
The experimental results demonstrate that the automated grasping system can obtain suitable models and generate successful grasps, even when the objects are not lying parallel to the supporting table.Abstract:
This paper describes the development of a novel vision-based modeling and grasping system for three-dimensional (3D) objects whose shape and location are unknown a priori. Our approach integrates online computer vision-based 3D object modeling with online 3D grasp planning and execution. A single wrist-mounted video camera is moved around the stationary object to obtain images from multiple viewpoints. Object silhouettes are extracted from these images and used to form a 3D solid model of the object. To refine the model, the object's top surface is modeled by scanning with a wrist-mounted line laser while recording images. The laser line in each image is used to form a 3D surface model that is combined with the silhouette result. The grasp planning algorithm is designed for the parallel-jaw grippers that are commonly used in industry. The algorithm analyses the solid model, generates a robust force closure grasp, and outputs the required gripper position and orientation for grasping the object. The robot then automatically picks up the object. Experiments are performed with two real-world 3D objects, a metal bracket and a hex nut. The shape, position and orientation of the objects are not known by the system a priori. The time required to compute an object model and plan a grasp was less than 4 s for each object. The experimental results demonstrate that the automated grasping system can obtain suitable models and generate successful grasps, even when the objects are not lying parallel to the supporting table.read more
Citations
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Journal ArticleDOI
Data-Driven Grasp Synthesis—A Survey
TL;DR: A review of the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps and an overview of the different methodologies are provided, which draw a parallel to the classical approaches that rely on analytic formulations.
Proceedings ArticleDOI
Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments
TL;DR: A framework for 3D geometric shape segmentation for close-range scenes used in mobile manipulation and grasping, out of sensed point cloud data is presented and a robust geometric mapping pipeline for large input datasets is proposed.
Journal ArticleDOI
Learning grasping points with shape context
Jeannette Bohg,Danica Kragic +1 more
TL;DR: The results show that a combination of a descriptor based on shape context with a non-linear classification algorithm leads to a stable detection of grasping points for a variety of objects.
Journal ArticleDOI
Data-driven grasping
Corey Goldfeder,Peter K. Allen +1 more
TL;DR: A novel framework for a data driven grasp planner that indexes partial sensor data into a database of 3D models with known grasps and transfersgrasps from those models to novel objects is proposed.
Journal ArticleDOI
Combined 2D-3D categorization and classification for multimodal perception systems
TL;DR: The system employs a library of specialized perception routines that solve different, well-defined perceptual sub-tasks and can be combined into composite perceptual activities including the construction of an object model database, multimodal object classification, and object model reconstruction for grasping.
References
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TL;DR: This paper presents fast and simple algorithms for directly constructing force-closure grasps based on the shape of the grasped object, and shows that most nonmarginal equilibriumGrasps are force- closuregrasps.
Journal ArticleDOI
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